OpenRaiser/Intern-Atlas huggingface dataset:
Mixed Reality Guidance of a Surgical Scalpel Using Magic Leap: Evaluation on a 3D-Printed Liver Phantom
Venue: arXiv cs.HC 2025
Year: 2025

Augmented and mixed reality (MR) systems have the potential to improve surgical precision by overlaying digital guidance directly onto the operative field. This paper presents a novel MR guidance system using the Magic Leap head-mounted display to assist surgeons in executing precise scalpel movements during liver surgery. The system projects holographic cues onto a patient-specific 3D-printed liver phantom, guiding resection along a predetermined path. We describe the system design, including preoperative modeling, registration of virtual content to the phantom, and real-time visualization through the Magic Leap device. In a controlled phantom study, surgical trainees performed resection tasks with and without MR guidance. Quantitative results demonstrated that MR guidance improved cutting accuracy (mean deviation from planned path was reduced from 5.0 mm without AR to 2.0 mm with AR guidance) and efficiency (mean task time decreased from 55 s to 32 s). These improvements of approximately 60% in accuracy and 40% in speed underscore the potential benefit of MR in surgical navigation. Participants reported that the Magic Leap visualization enhanced depth perception and confidence in locating tumor boundaries. This work provides a comprehensive evaluation of an MR-assisted surgical guidance approach, highlighting its feasibility on a realistic organ phantom. We discuss the technical challenges (registration accuracy, line-of-sight, user ergonomics) and outline future steps toward clinical translation. The results suggest that Magic Leap-based MR guidance can significantly augment a surgeon's performance in delicate resection tasks, paving the way for safer and more precise liver surgery.
Generating High-Level Test Cases from Requirements using LLM: An Industry Study
Venue: arXiv cs.SE 2025
Year: 2025

Currently, generating high-level test cases described in natural language from requirement documents is performed manually. In the industry, including companies specializing in software testing, there is a significant demand for the automatic generation of high-level test cases from requirement documents using Large Language Models (LLMs). Efforts to utilize LLMs for requirement analysis are underway. In some cases, retrieval-augmented generation (RAG) is employed for generating high-level test cases using LLMs. However, in practical applications, it is necessary to create a RAG tailored to the knowledge system of each specific application, which is labor-intensive. Moreover, when applying high-level test case generation as a prompt, there is no established method for instructing the generation of high-level test cases at a level applicable to other specifications without using RAG. It is required to establish a method for the automatic generation of high-level test cases that can be generalized across a wider range of requirement documents. In this paper, we propose a method for generating high-level (GHL) test cases from requirement documents using only prompts, without creating RAGs. In the proposed method, first, the requirement document is input into the LLM to generate test design techniques corresponding to the requirement document. Then, high-level test cases are generated for each of the generated test design techniques. Furthermore, we verify an evaluation method based on semantic similarity of the generated high-level test cases. In the experiments, we confirmed the method using datasets from Bluetooth and Mozilla, where requirement documents and high-level test cases are available, achieving macro-recall measurement of 0.81 and 0.37, respectively. We believe that the method is feasible for practical application in generating high-level test cases without using RAG.
Does higher interpretability imply better utility? A Pairwise Analysis on Sparse Autoencoders
Venue: arXiv cs.LG 2025
Year: 2025

Sparse Autoencoders (SAEs) are widely used to steer large language models (LLMs), based on the assumption that their interpretable features naturally enable effective model behavior steering. Yet, a fundamental question remains unanswered: does higher interpretability indeed imply better steering utility? To answer this question, we train 90 SAEs across three LLMs (Gemma-2-2B, Qwen-2.5-3B, Gemma-2-9B), spanning five architectures and six sparsity levels, and evaluate their interpretability and steering utility based on SAEBench (arXiv:2501.12345) and AxBench (arXiv:2502.23456) respectively, and perform a rank-agreement analysis via Kendall's rank coefficients (tau b). Our analysis reveals only a relatively weak positive association (tau b approx 0.298), indicating that interpretability is an insufficient proxy for steering performance. We conjecture the interpretability utility gap may stem from the selection of SAE features, as not all of them are equally effective for steering. To further find features that truly steer the behavior of LLMs, we propose a novel selection criterion called Delta Token Confidence, which measures how much amplifying a feature changes the next token distribution. We show that our method improves the steering performance of three LLMs by 52.52 percent compared to the current best output score based criterion (arXiv:2503.34567). Strikingly, after selecting features with high Delta Token Confidence, the correlation between interpretability and utility vanishes (tau b approx 0), and can even become negative. This further highlights the divergence between interpretability and utility for the most effective steering features.
Group Policy Gradient
Venue: arXiv cs.LG 2025
Year: 2025

We introduce Group Policy Gradient (GPG), a family of critic-free policy-gradient estimators for general MDPs. Inspired by the success of GRPO's approach in Reinforcement Learning from Human Feedback (RLHF), GPG replaces a learned value function with a group-based Monte Carlo advantage estimator, removing the memory, compute, and hyperparameter costs of training a critic while preserving PPO's clipped-objective structure. We prove the consistency of the GPG estimator, analyze the bias-variance tradeoffs, and demonstrate empirically that GPG matches or outperforms PPO on standard benchmarks. GPG makes better use of parallel simulations, which, together with its critic-free design, results in more efficient use of computational resources than PPO.
A Time-Bound Signature Scheme for Blockchains
Venue: arXiv cs.CR 2025
Year: 2025

We introduce a modified Schnorr signature scheme to allow for time-bound signatures for transaction fee auction bidding and smart contract purposes in a blockchain context, ensuring an honest producer can only validate a signature before a given block height. The immutable blockchain is used as a source of universal time for the signature scheme. We show the use of such a signature scheme leads to lower MEV revenue for builders. We then apply our time-bound signatures to Ethereum's EIP-1559 and show how it can be used to mitigate the effect of MEV on predicted equilibrium strategies.
A Position- and Energy-Aware Routing Strategy for Subterranean LoRa Mesh Networks
Venue: arXiv cs.NI 2025
Year: 2025

Although LoRa is predominantly employed with the single-hop LoRaWAN protocol, recent advancements have extended its application to multi-hop mesh topologies. Designing efficient routing for LoRa mesh networks remains challenging due to LoRa's low data rate and ALOHA-based MAC. Prior work often adapts conventional protocols for low-traffic, aboveground networks with strict duty cycle constraints or uses flooding-based methods in subterranean environments. However, these approaches inefficiently utilize the limited available network bandwidth in these low-data-rate networks due to excessive control overhead, acknowledgments, and redundant retransmissions. In this paper, we introduce a novel position- and energy-aware routing strategy tailored for subterranean LoRa mesh networks aimed at enhancing maximum throughput and power efficiency while also maintaining high packet delivery ratios. Our mechanism begins with a lightweight position learning phase, during which LoRa repeaters ascertain their relative positions and gather routing information. Afterwards, the network becomes fully operational with adaptive routing, leveraging standby LoRa repeaters for recovery from packet collisions and losses, and energy-aware route switching to balance battery depletion across repeaters. The simulation results on a representative subterranean network demonstrate a 185% increase in maximum throughput and a 75% reduction in energy consumption compared to a previously optimized flooding-based approach for high traffic.
Shrinking the Kernel Attack Surface Through Static and Dynamic Syscall Limitation
Venue: arXiv cs.CR 2025
Year: 2025

Linux Seccomp is widely used by the program developers and the system maintainers to secure the operating systems, which can block unused syscalls for different applications and containers to shrink the attack surface of the operating systems. However, it is difficult to configure the whitelist of a container or application without the help of program developers. Docker containers block about only 50 syscalls by default, and lots of unblocked useless syscalls introduce a big kernel attack surface. To obtain the dependent syscalls, dynamic tracking is a straight-forward approach but it cannot get the full syscall list. Static analysis can construct an over-approximated syscall list, but the list contains many false positives. In this paper, a systematic dependent syscall analysis approach, sysverify, is proposed by combining static analysis and dynamic verification together to shrink the kernel attack surface. The semantic gap between the binary executables and syscalls is bridged by analyzing the binary and the source code, which builds the mapping between the library APIs and syscalls systematically. To further reduce the attack surface at best effort, we propose a dynamic verification approach to intercept and analyze the security of the invocations of indirect-call-related or rarely invoked syscalls with low overhead.
Balancing Interpretability and Performance in Reinforcement Learning: An Adaptive Spectral Based Linear Approach
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) has been widely applied to sequential decision making, where interpretability and performance are both critical for practical adoption. Current approaches typically focus on performance and rely on post hoc explanations to account for interpretability. Different from these approaches, we focus on designing an interpretability-oriented yet performance-enhanced RL approach. Specifically, we propose a spectral based linear RL method that extends the ridge regression-based approach through a spectral filter function. The proposed method clarifies the role of regularization in controlling estimation error and further enables the design of an adaptive regularization parameter selection strategy guided by the bias-variance trade-off principle. Theoretical analysis establishes near-optimal bounds for both parameter estimation and generalization error. Extensive experiments on simulated environments and real-world datasets from Kuaishou and Taobao demonstrate that our method either outperforms or matches existing baselines in decision quality. We also conduct interpretability analyses to illustrate how the learned policies make decisions, thereby enhancing user trust. These results highlight the potential of our approach to bridge the gap between RL theory and practical decision making, providing interpretability, accuracy, and adaptability in management contexts.
Mapping Rio de Janeiro's favelas: general-purpose vs. satellite-specific neural networks
Venue: arXiv cs.CV 2025
Year: 2025

While deep learning methods for detecting informal settlements have already been developed, they have not yet fully utilized the potential offered by recent pretrained neural networks. We compare two types of pretrained neural networks for detecting the favelas of Rio de Janeiro: 1. Generic networks pretrained on large diverse datasets of unspecific images, 2. A specialized network pretrained on satellite imagery. While the latter is more specific to the target task, the former has been pretrained on significantly more images. Hence, this research investigates whether task specificity or data volume yields superior performance in urban informal settlement detection.
Soft Disentanglement in Frequency Bands for Neural Audio Codecs
Venue: arXiv cs.SD 2025
Year: 2025

In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement methods often rely on assumptions that are highly dependent on data characteristics or specific tasks. In this work, we introduce a generalizable approach for learning disentangled features within a neural architecture. Our method applies spectral decomposition to time-domain signals, followed by a multi-branch audio codec that operates on the decomposed components. Empirical evaluations demonstrate that our approach achieves better reconstruction and perceptual performance compared to a state-of-the-art baseline while also offering potential advantages for inpainting tasks.
Neural Low-Discrepancy Sequences
Venue: arXiv cs.LG 2025
Year: 2025

Low-discrepancy points are designed to efficiently fill the space in a uniform manner. This uniformity is highly advantageous in many problems in science and engineering, including in numerical integration, computer vision, machine perception, computer graphics, machine learning, and simulation. Whereas most previous low-discrepancy constructions rely on abstract algebra and number theory, Message-Passing Monte Carlo (MPMC) was recently introduced to exploit machine learning methods for generating point sets with lower discrepancy than previously possible. However, MPMC is limited to generating point sets and cannot be extended to low-discrepancy sequences (LDS), i.e., sequences of points in which every prefix has low discrepancy, a property essential for many applications. To address this limitation, we introduce Neural Low-Discrepancy Sequences ($NeuroLDS$), the first machine learning-based framework for generating LDS. Drawing inspiration from classical LDS, we train a neural network to map indices to points such that the resulting sequences exhibit minimal discrepancy across all prefixes. To this end, we deploy a two-stage learning process: supervised approximation of classical constructions followed by unsupervised fine-tuning to minimize prefix discrepancies. We demonstrate that $NeuroLDS$ outperforms all previous LDS constructions by a significant margin with respect to discrepancy measures. Moreover, we demonstrate the effectiveness of $NeuroLDS$ across diverse applications, including numerical integration, robot motion planning, and scientific machine learning. These results highlight the promise and broad significance of Neural Low-Discrepancy Sequences. Our code can be found at https://github.com/camail-official/neuro-lds.
You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

The widespread use of preprint repositories such as arXiv has accelerated the communication of scientific results but also introduced overlooked security risks. Beyond PDFs, these platforms provide unrestricted access to original source materials, including LaTeX sources, auxiliary code, figures, and embedded comments. In the absence of sanitization, submissions may disclose sensitive information that adversaries can harvest using open-source intelligence. In this work, we present the first large-scale security audit of preprint archives, analyzing more than 1.2 TB of source data from 100,000 arXiv submissions. We introduce LaTeXpOsEd, a four-stage framework that integrates pattern matching, logical filtering, traditional harvesting techniques, and large language models (LLMs) to uncover hidden disclosures within non-referenced files and LaTeX comments. To evaluate LLMs' secret-detection capabilities, we introduce LLMSec-DB, a benchmark on which we tested 25 state-of-the-art models. Our analysis uncovered thousands of PII leaks, GPS-tagged EXIF files, publicly available Google Drive and Dropbox folders, editable private SharePoint links, exposed GitHub and Google credentials, and cloud API keys. We also uncovered confidential author communications, internal disagreements, and conference submission credentials, exposing information that poses serious reputational risks to both researchers and institutions. We urge the research community and repository operators to take immediate action to close these hidden security gaps. To support open science, we release all scripts and methods from this study but withhold sensitive findings that could be misused, in line with ethical principles. The source code and related material are available at the project website https://github.com/LaTeXpOsEd
Rezwan: Leveraging Large Language Models for Comprehensive Hadith Text Processing: A 1.2M Corpus Development
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents the development of Rezwan, a large-scale AI-assisted Hadith corpus comprising over 1.2M narrations, extracted and structured through a fully automated pipeline. Building on digital repositories such as Maktabat Ahl al-Bayt, the pipeline employs Large Language Models (LLMs) for segmentation, chain--text separation, validation, and multi-layer enrichment. Each narration is enhanced with machine translation into twelve languages, intelligent diacritization, abstractive summarization, thematic tagging, and cross-text semantic analysis. This multi-step process transforms raw text into a richly annotated research-ready infrastructure for digital humanities and Islamic studies. A rigorous evaluation was conducted on 1,213 randomly sampled narrations, assessed by six domain experts. Results show near-human accuracy in structured tasks such as chain--text separation (9.33/10) and summarization (9.33/10), while highlighting ongoing challenges in diacritization and semantic similarity detection. Comparative analysis against the manually curated Noor Corpus demonstrates the superiority of Najm in both scale and quality, with a mean overall score of 8.46/10 versus 3.66/10. Furthermore, cost analysis confirms the economic feasibility of the AI approach: tasks requiring over 229,000 hours of expert labor were completed within months at a fraction of the cost. The work introduces a new paradigm in religious text processing by showing how AI can augment human expertise, enabling large-scale, multilingual, and semantically enriched access to Islamic heritage.
Allocation of Parameters in Transformers
Venue: arXiv cs.LG 2025
Year: 2025

Transformers have achieved remarkable successes across a wide range of applications, yet the theoretical foundation of their model efficiency remains underexplored. In this work, we investigate how the model parameters -- mainly attention heads and head dimensions -- should be allocated across layers to balance expressivity and efficiency. We first provide mathematical analysis on the role of early layers in information extraction from an approximation perspective, with a theoretical characterization on the trade-off between the number of heads and head dimension under a fixed parameter budget. In addition, we uncover and prove the \emph{saturation} behavior of softmax activations: Continuously increasing head dimensions can lead to diminishing returns in learning errors, particularly for long sequences. Supported by both theory and experiments, this saturation pattern suggests that later layers can operate more efficiently with reduced parameters. Combining these insights, we propose principled strategies for allocating attention heads and dimensions across Transformers' layers, shedding light on theoretically-grounded model efficiency of Transformer-based architectures.
Lightweight and Data-Efficient MultivariateTime Series Forecasting using Residual-Stacked Gaussian (RS-GLinear) Architecture
Venue: arXiv cs.CE 2025
Year: 2025

Following the success of Transformer architectures in language modeling, particularly their ability to capture long-range dependencies, researchers have explored how these architectures can be adapted for time-series forecasting. Transformer-based models have been proposed to handle both short- and long-term dependencies when predicting future values from historical data. However, studies such as those by Zeng et al. (2022) and Rizvi et al. (2025) have reported mixed results in long-term forecasting tasks. In this work, we evaluate the Gaussian-based Linear architecture introduced by Rizvi et al. (2025) and present an enhanced version called the Residual Stacked Gaussian Linear (RSGL) model. We also investigate the broader applicability of the RSGL model in additional domains, including financial time series and epidemiological data. Experimental results show that the RSGL model achieves improved prediction accuracy and robustness compared to both the baseline Gaussian Linear and Transformer-based models.
The Hidden Game Problem
Venue: arXiv cs.AI 2025
Year: 2025

This paper investigates a class of games with large strategy spaces, motivated by challenges in AI alignment and language games. We introduce the hidden game problem, where for each player, an unknown subset of strategies consistently yields higher rewards compared to the rest. The central question is whether efficient regret minimization algorithms can be designed to discover and exploit such hidden structures, leading to equilibrium in these subgames while maintaining rationality in general. We answer this question affirmatively by developing a composition of regret minimization techniques that achieve optimal external and swap regret bounds. Our approach ensures rapid convergence to correlated equilibria in hidden subgames, leveraging the hidden game structure for improved computational efficiency.
Optimized Minimal 4D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

4D Gaussian Splatting has emerged as a new paradigm for dynamic scene representation, enabling real-time rendering of scenes with complex motions. However, it faces a major challenge of storage overhead, as millions of Gaussians are required for high-fidelity reconstruction. While several studies have attempted to alleviate this memory burden, they still face limitations in compression ratio or visual quality. In this work, we present OMG4 (Optimized Minimal 4D Gaussian Splatting), a framework that constructs a compact set of salient Gaussians capable of faithfully representing 4D Gaussian models. Our method progressively prunes Gaussians in three stages: (1) Gaussian Sampling to identify primitives critical to reconstruction fidelity, (2) Gaussian Pruning to remove redundancies, and (3) Gaussian Merging to fuse primitives with similar characteristics. In addition, we integrate implicit appearance compression and generalize Sub-Vector Quantization (SVQ) to 4D representations, further reducing storage while preserving quality. Extensive experiments on standard benchmark datasets demonstrate that OMG4 significantly outperforms recent state-of-the-art methods, reducing model sizes by over 60% while maintaining reconstruction quality. These results position OMG4 as a significant step forward in compact 4D scene representation, opening new possibilities for a wide range of applications. Our source code is available at https://minshirley.github.io/OMG4/.